Conversation Flow Control: When Users Don’t Follow Your Script
Source: Dev.to
Why Traditional Flow Design Fails
Most bots are built like phone menus:
Question → Answer → Next question → Next answer …This works only when users behave like obedient robots. Real humans:
- Jump ahead and ask for price immediately.
- Jump backward and ask what courses exist again.
- Go sideways and ask about refund policy before deciding.
- Overload a single message with multiple intents, preferences, and questions.
A rigid flow collapses the moment users act like humans.
The Five Behaviors That Break Linear Bots
-
Information dumping – A user provides course interest, experience level, schedule preference, budget, and start timing in one message.
Linear bot ignores most of it and asks the next scripted question, making the user feel unheard. -
Topic jumping – Bot asks about experience level, user asks about job placement.
Strict bot blocks the user and demands the original answer → abandonment. -
Pre‑emptive questions – User asks about price while the bot is still collecting background info.
If the bot refuses to answer until the flow is followed, the user assumes the bot is useless. -
Vague input – User says “something with computers” or “show me all options.”
If the bot can’t guide them without forcing a narrow choice, the conversation stalls. -
Multi‑intent messages – Enrollment intent, pricing, schedule, refund, and delivery mode packed into one line.
Answering only one part makes the user feel ignored; repeated occurrences lead to predictable abandonment.
Result: 88 % of conversations derailed within three messages.
What Didn’t Work
| Attempt | Description | Outcome |
|---|---|---|
| Strict script enforcement | Bot forced users to answer questions in order. | Slight increase in completion, but users felt controlled and abandoned. |
| Free‑form conversation | Bot tried to handle anything at any time. | Got lost, gave irrelevant responses, lacked a control mechanism. |
| Intent detection without context | Detected intent per message only. | Misunderstood user intent because it didn’t remember prior context. |
The Breakthrough: Adaptive Flow Management
We realized users need a guide that adapts, not a rigid script.
Three‑layer flow‑control system
-
Information extraction
- Extract whatever information the user provides, regardless of order.
- Store all detected entities (course, schedule, budget, etc.) and only ask for missing pieces.
-
Dynamic priority routing
- If the user asks a direct question (price, refund, installments), answer it immediately, even “out of order.”
- After answering, gently return to the enrollment flow.
-
Context‑aware responses
- Remember what the user said earlier and what has already been answered.
- Avoid repeating questions – the bot “listens” instead of being useless.
A Real Example of Adaptive Flow
-
User: “I’m thinking about data science or machine learning, need weekend batches, and what’s the price range?”
Bot: Answers all three parts, then asks a relevant follow‑up about the user’s background. -
User: “What’s the refund policy?”
Bot: Answers immediately, then returns to the course‑fit question without restarting. -
User: “I know a bit of Python.”
Bot: Uses that context to recommend the best starting path and offers curriculum details. -
User: “Do you have installment options?”
Bot: Provides installment details inline and moves directly to enrollment.
Result: No friction, no forced order, no repetition.
Edge Cases That Matter
| Situation | Desired Bot Behavior |
|---|---|
| Repeated question (different wording) | Acknowledge, confirm the previous answer, ask if the concern is still a blocker. |
| Off‑topic digression | Acknowledge politely, then gently redirect back to the enrollment flow. |
| Incomplete answer (“not much”) | Clarify in a friendly way instead of assuming. |
| Stuck choosing between options | Ask a single, simple preference question that reveals the right path. |
| Rapid‑fire questions | Respond with a compact set of answers in one message rather than cherry‑picking one. |
Results
| Metric | Before Adaptive Flow | After Adaptive Flow |
|---|---|---|
| Conversation completion rate | 12 % | 78 % |
| Average messages before abandonment | ~4 | >10 |
| User sentiment | “The bot doesn’t listen.” | Positive – “The bot feels helpful.” |
The adaptive flow turned a failing chatbot into a high‑performing enrollment assistant, dramatically closing the gap between bot and human conversion rates.
Recent Success
- Enrollment conversion reached 71 %.
- Complaints dropped sharply.
- Feedback shifted from frustration to surprise that the bot “actually understood everything.”
The Core Lesson
Humans don’t communicate linearly. They communicate in bursts, tangents, interruptions, and multi‑intent messages.
- If your bot forces humans into a script, conversion will stay low.
- If your bot adapts to human behavior, conversion climbs.
We didn’t improve results by writing a better script. We improved results by throwing away the script and building an adaptive system that meets users where they are.
Your Turn
- How do you handle users who don’t follow your chatbot’s intended flow?
- What’s your biggest challenge with flow management?
- Have you dealt with multi‑intent messages breaking your bot?
Written by Farhan Habib Faraz
Senior Prompt Engineer and Team Lead at PowerInAI
Building AI automation that adapts to humans
Tags: conversationalai, flowcontrol, chatbot, ux, nlp, promptengineering